{"title":"咖啡因成瘾预测的模糊界面系统","authors":"Archit Aggarwal, Garima Aggrawal","doi":"10.1109/Confluence47617.2020.9058235","DOIUrl":null,"url":null,"abstract":"Caffeine is a stimulant which enables the prevention or delay of drowsiness or a feeling of sleepiness. Caffeine is an unregulated substance in most parts of the world and hence poses a threat of addiction. The symptoms of caffeine addiction and withdrawal are defined well but are large in number and sometimes inseparable from the same symptoms of other conditions. Fuzzy logic can be used to combine many such symptoms and arrive at a certain conclusion. This paper aims to implement fuzzy logic to predict the risk caffeine addiction in functioning adults based on certain predictors. The system takes into account four such predictors. The proposed model gives adequate results with an accuracy of eighty to hundred percent under different scenarios.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Fuzzy Interface System for the Prediction of Caffeine Addiction\",\"authors\":\"Archit Aggarwal, Garima Aggrawal\",\"doi\":\"10.1109/Confluence47617.2020.9058235\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Caffeine is a stimulant which enables the prevention or delay of drowsiness or a feeling of sleepiness. Caffeine is an unregulated substance in most parts of the world and hence poses a threat of addiction. The symptoms of caffeine addiction and withdrawal are defined well but are large in number and sometimes inseparable from the same symptoms of other conditions. Fuzzy logic can be used to combine many such symptoms and arrive at a certain conclusion. This paper aims to implement fuzzy logic to predict the risk caffeine addiction in functioning adults based on certain predictors. The system takes into account four such predictors. The proposed model gives adequate results with an accuracy of eighty to hundred percent under different scenarios.\",\"PeriodicalId\":180005,\"journal\":{\"name\":\"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/Confluence47617.2020.9058235\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/Confluence47617.2020.9058235","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Fuzzy Interface System for the Prediction of Caffeine Addiction
Caffeine is a stimulant which enables the prevention or delay of drowsiness or a feeling of sleepiness. Caffeine is an unregulated substance in most parts of the world and hence poses a threat of addiction. The symptoms of caffeine addiction and withdrawal are defined well but are large in number and sometimes inseparable from the same symptoms of other conditions. Fuzzy logic can be used to combine many such symptoms and arrive at a certain conclusion. This paper aims to implement fuzzy logic to predict the risk caffeine addiction in functioning adults based on certain predictors. The system takes into account four such predictors. The proposed model gives adequate results with an accuracy of eighty to hundred percent under different scenarios.